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Arc Institute 的 Science Fellow 周景天及其团队在新预印本中介绍 Bolero，这是一个以细胞状态为条件、仅从 DNA 预测染色质可及性与基因表达的模型，解释了相同 DNA 在不同细胞中的差异解读。
文章比较了 DNA 语言模型（如 Evo 2）与 AlphaGenome 在预测 DNA 变异影响上的差异：前者基于序列似然评分，后者基于实验测量预测基因表达与剪接等读出，且仅限研究用途。
MIT researchers led by Pulin Li developed IRIS, a neural network model that detects unique gene-activity 'fingerprints' left by cellular signaling pathways, allowing them to reconstruct a cell's signaling history across many cell types using transfer learning. Published in Nature Methods, the approach could accelerate stem cell engineering, organoid development, and disease research.
GATE-ST proposes a gene-aware text-image encoder that integrates text-encoded gene summaries with histology image embeddings via cross-attention to improve spatial gene expression prediction, outperforming image-only and random-embedding baselines in pathology imaging benchmarks.
SpaFactor is a lightweight framework that predicts spatial transcriptomics from histology images by integrating spatial context and gene programs, demonstrating improved accuracy and biological fidelity across multiple cohorts.
The paper proposes CorrFlow, a correlation-guided flow matching framework for predicting spatial transcriptomics from histology images, explicitly modeling gene-gene dependencies to improve biological coherence in generated profiles.
MIT Technology Review profiles Deanne Taylor, a bioinformatics director who pushed for pediatric representation in the Human Cell Atlas and helped launch the dGTEx project to map healthy gene expression in children, addressing the lack of baseline data for pediatric medicine.
This paper introduces CoCoS, a contrastive pretraining framework that learns whole-cell representations from complementary transcriptomic views, addressing limitations of masked gene reconstruction in single-cell foundation models. Experiments on cell-type annotation and gene regulatory network inference show competitive transfer performance.
This paper proposes JoPMol, a jointly controlled precision molecular generative model that integrates gene expression profiles, molecular structure text, and chemical properties to generate personalized drug candidates, outperforming state-of-the-art methods.
COAST is a context-aware differential learning framework for predicting spatial gene expression from H&E histopathology images, using joint absolute and signed differential regression to capture spatial relationships.
The Hsu and Konermann labs at Arc Institute developed COMBINE, the first platform for systematically testing combinatorial interactions of epigenetic readers, writers, and erasers, screening over 50,000 effector pairs.
This paper presents the first unified benchmark for pathway-guided therapy response modeling, evaluating three biologically informed architectures (BINN, GraphPath, PATH) across five cancer cohorts from The Cancer Genome Atlas for multi-label prediction of targeted therapy, radiation therapy, and survival outcomes.
This paper introduces VCR-Agent, a multi-agent framework that enhances large language models for biological research by generating and validating mechanistic explanations using structured formalism and the VC-TRACES dataset. The approach improves factual precision in gene expression prediction through verified mechanistic reasoning in virtual cells.